Overview

Dataset statistics

Number of variables23
Number of observations3678
Missing cells6835
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory632.1 B

Variable types

Categorical10
Text3
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
balcony is highly overall correlated with bathroom and 1 other fieldsHigh correlation
bathroom is highly overall correlated with area and 6 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
floorNum is highly overall correlated with property_typeHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.7%)Imbalance
floorNum has 143 (3.9%) missing valuesMissing
facing has 1045 (28.4%) missing valuesMissing
super_built_up_area has 1802 (49.0%) missing valuesMissing
built_up_area has 1987 (54.0%) missing valuesMissing
carpet_area has 1806 (49.1%) missing valuesMissing
area is highly skewed (γ1 = 29.73502311)Skewed
built_up_area is highly skewed (γ1 = 40.71859279)Skewed
carpet_area is highly skewed (γ1 = 24.33323909)Skewed
luxury_score has 462 (12.6%) zerosZeros

Reproduction

Analysis started2024-01-21 03:35:04.351883
Analysis finished2024-01-21 03:35:13.408404
Duration9.06 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

property_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.7 KiB
flat
2819 
house
859 

Length

Max length5
Median length4
Mean length4.2335508
Min length4

Characters and Unicode

Total characters15571
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Length

2024-01-20T22:35:13.461265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T22:35:13.523974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15571
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15571
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%
Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size294.0 KiB
2024-01-20T22:35:13.735163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.867827
Min length1

Characters and Unicode

Total characters62023
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowvatika gurgaon
2nd rowrof aalayas
3rd rowbestech park view residency
4th rowla vida by tata housing
5th rowm3m heights
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.5%
heights 134
 
1.4%
Other values (783) 7499
77.5%
2024-01-20T22:35:14.061816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6712
 
10.8%
6004
 
9.7%
a 5861
 
9.4%
r 4172
 
6.7%
n 4164
 
6.7%
i 3831
 
6.2%
t 3719
 
6.0%
s 3473
 
5.6%
l 2944
 
4.7%
o 2755
 
4.4%
Other values (31) 18388
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55474
89.4%
Space Separator 6004
 
9.7%
Decimal Number 527
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6712
12.1%
a 5861
 
10.6%
r 4172
 
7.5%
n 4164
 
7.5%
i 3831
 
6.9%
t 3719
 
6.7%
s 3473
 
6.3%
l 2944
 
5.3%
o 2755
 
5.0%
d 2489
 
4.5%
Other values (16) 15354
27.7%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
9 13
 
2.5%
0 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6004
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55474
89.4%
Common 6549
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6712
12.1%
a 5861
 
10.6%
r 4172
 
7.5%
n 4164
 
7.5%
i 3831
 
6.9%
t 3719
 
6.7%
s 3473
 
6.3%
l 2944
 
5.3%
o 2755
 
5.0%
d 2489
 
4.5%
Other values (16) 15354
27.7%
Common
ValueCountFrequency (%)
6004
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
9 13
 
0.2%
0 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6712
 
10.8%
6004
 
9.7%
a 5861
 
9.4%
r 4172
 
6.7%
n 4164
 
6.7%
i 3831
 
6.2%
t 3719
 
6.0%
s 3473
 
5.6%
l 2944
 
4.7%
o 2755
 
4.4%
Other values (31) 18388
29.6%

sector
Text

Distinct115
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.9 KiB
2024-01-20T22:35:14.221364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3175639
Min length3

Characters and Unicode

Total characters34270
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 83
2nd rowsector 102
3rd rowsector 2
4th rowsector 113
5th rowsector 65
ValueCountFrequency (%)
sector 3449
46.7%
road 178
 
2.4%
sohna 166
 
2.2%
85 108
 
1.5%
102 106
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 88
 
1.2%
81 87
 
1.2%
65 87
 
1.2%
Other values (107) 2923
39.6%
2024-01-20T22:35:14.437064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3804
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3500
10.2%
t 3460
10.1%
1 1074
 
3.1%
0 802
 
2.3%
8 780
 
2.3%
Other values (21) 6207
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23306
68.0%
Decimal Number 7257
 
21.2%
Space Separator 3707
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3804
16.3%
s 3694
15.8%
r 3694
15.8%
e 3548
15.2%
c 3500
15.0%
t 3460
14.8%
a 698
 
3.0%
d 248
 
1.1%
n 230
 
1.0%
h 203
 
0.9%
Other values (10) 227
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1074
14.8%
0 802
11.1%
8 780
10.7%
9 762
10.5%
6 741
10.2%
7 682
9.4%
2 678
9.3%
3 663
9.1%
5 592
8.2%
4 483
6.7%
Space Separator
ValueCountFrequency (%)
3707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23306
68.0%
Common 10964
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3804
16.3%
s 3694
15.8%
r 3694
15.8%
e 3548
15.2%
c 3500
15.0%
t 3460
14.8%
a 698
 
3.0%
d 248
 
1.1%
n 230
 
1.0%
h 203
 
0.9%
Other values (10) 227
 
1.0%
Common
ValueCountFrequency (%)
3707
33.8%
1 1074
 
9.8%
0 802
 
7.3%
8 780
 
7.1%
9 762
 
7.0%
6 741
 
6.8%
7 682
 
6.2%
2 678
 
6.2%
3 663
 
6.0%
5 592
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3804
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3500
10.2%
t 3460
10.1%
1 1074
 
3.1%
0 802
 
2.3%
8 780
 
2.3%
Other values (21) 6207
18.1%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5332177
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-20T22:35:14.530002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9803386
Coefficient of variation (CV)1.1765031
Kurtosis14.937313
Mean2.5332177
Median Absolute Deviation (MAD)0.72
Skewness3.2796187
Sum9274.11
Variance8.8824181
MonotonicityNot monotonic
2024-01-20T22:35:14.600691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.5 64
 
1.7%
1.2 64
 
1.7%
0.9 64
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.5%
2 52
 
1.4%
0.95 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.1%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13890.407
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-20T22:35:14.824660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4716
Q16815
median9020
Q313878
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063

Descriptive statistics

Standard deviation23207.3
Coefficient of variation (CV)1.6707429
Kurtosis186.9711
Mean13890.407
Median Absolute Deviation (MAD)2795
Skewness11.438435
Sum50852780
Variance5.3857875 × 108
MonotonicityNot monotonic
2024-01-20T22:35:14.895667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
22222 13
 
0.4%
11111 13
 
0.4%
6666 13
 
0.4%
7500 12
 
0.3%
8333 12
 
0.3%
33333 11
 
0.3%
Other values (2641) 3510
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2559
Distinct (%)69.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2888.0328
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-20T22:35:14.972769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519.06
Q11232.6
median1733
Q32300.09
95-th percentile4245.68
Maximum875000
Range874950
Interquartile range (IQR)1067.49

Descriptive statistics

Standard deviation23164.348
Coefficient of variation (CV)8.0208049
Kurtosis942.2872
Mean2888.0328
Median Absolute Deviation (MAD)532.93
Skewness29.735023
Sum10573088
Variance5.3658701 × 108
MonotonicityNot monotonic
2024-01-20T22:35:15.045061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3240 43
 
1.2%
2700 36
 
1.0%
2000 33
 
0.9%
1800 32
 
0.9%
900 28
 
0.8%
1350 20
 
0.5%
2250 20
 
0.5%
1000 18
 
0.5%
1650.17 17
 
0.5%
4500 17
 
0.5%
Other values (2549) 3397
92.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857.14 1
< 0.1%
620000 1
< 0.1%
566666.67 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517.24 2
0.1%
65261 1
< 0.1%
58227.85 1
< 0.1%
Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.3 KiB
2024-01-20T22:35:15.246648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.242795
Min length12

Characters and Unicode

Total characters199505
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1848 ?
Unique (%)50.2%

Sample

1st rowSuper Built up area 1735(161.19 sq.m.)
2nd rowSuper Built up area 597(55.46 sq.m.)Carpet area: 535.4 sq.ft. (49.74 sq.m.)
3rd rowSuper Built up area 1780(165.37 sq.m.)
4th rowSuper Built up area 1579(146.69 sq.m.)Built Up area: 1420 sq.ft. (131.92 sq.m.)Carpet area: 1220 sq.ft. (113.34 sq.m.)
5th rowBuilt Up area: 1433 (133.13 sq.m.)
ValueCountFrequency (%)
area 5575
18.5%
sq.m 3656
12.1%
up 3022
 
10.0%
built 2317
 
7.7%
super 1876
 
6.2%
sq.ft 1752
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 703
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8703
28.9%
2024-01-20T22:35:15.546273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26475
 
13.3%
. 20397
 
10.2%
a 13158
 
6.6%
r 9459
 
4.7%
e 9323
 
4.7%
1 9209
 
4.6%
s 7570
 
3.8%
q 7434
 
3.7%
t 7327
 
3.7%
u 6774
 
3.4%
Other values (25) 82379
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82791
41.5%
Decimal Number 47153
23.6%
Space Separator 26475
 
13.3%
Other Punctuation 23415
 
11.7%
Uppercase Letter 8597
 
4.3%
Close Punctuation 5537
 
2.8%
Open Punctuation 5537
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13158
15.9%
r 9459
11.4%
e 9323
11.3%
s 7570
9.1%
q 7434
9.0%
t 7327
8.8%
u 6774
8.2%
p 6770
8.2%
m 5546
6.7%
l 3703
 
4.5%
Other values (5) 5727
6.9%
Decimal Number
ValueCountFrequency (%)
1 9209
19.5%
0 6631
14.1%
2 5689
12.1%
5 4716
10.0%
3 3963
8.4%
4 3712
7.9%
6 3675
 
7.8%
7 3254
 
6.9%
8 3158
 
6.7%
9 3146
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3022
35.2%
S 1876
21.8%
C 1872
21.8%
U 1146
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20397
87.1%
: 3018
 
12.9%
Space Separator
ValueCountFrequency (%)
26475
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5537
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5537
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108117
54.2%
Latin 91388
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13158
14.4%
r 9459
10.4%
e 9323
10.2%
s 7570
8.3%
q 7434
8.1%
t 7327
8.0%
u 6774
7.4%
p 6770
7.4%
m 5546
 
6.1%
l 3703
 
4.1%
Other values (10) 14324
15.7%
Common
ValueCountFrequency (%)
26475
24.5%
. 20397
18.9%
1 9209
 
8.5%
0 6631
 
6.1%
2 5689
 
5.3%
) 5537
 
5.1%
( 5537
 
5.1%
5 4716
 
4.4%
3 3963
 
3.7%
4 3712
 
3.4%
Other values (5) 16251
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26475
 
13.3%
. 20397
 
10.2%
a 13158
 
6.6%
r 9459
 
4.7%
e 9323
 
4.7%
1 9209
 
4.6%
s 7570
 
3.8%
q 7434
 
3.7%
t 7327
 
3.7%
u 6774
 
3.4%
Other values (25) 82379
41.3%

bedRoom
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3599782
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-20T22:35:15.625540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8973801
Coefficient of variation (CV)0.56470012
Kurtosis18.218941
Mean3.3599782
Median Absolute Deviation (MAD)1
Skewness3.4857171
Sum12358
Variance3.6000513
MonotonicityNot monotonic
2024-01-20T22:35:15.685560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1497
40.7%
2 942
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
12 28
 
0.8%
7 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1497
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4244154
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-20T22:35:15.750452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9478158
Coefficient of variation (CV)0.56880241
Kurtosis17.548118
Mean3.4244154
Median Absolute Deviation (MAD)1
Skewness3.2493833
Sum12595
Variance3.7939863
MonotonicityNot monotonic
2024-01-20T22:35:15.809343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1078
29.3%
2 1047
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1078
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
3+
1172 
3
1075 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3186514
Min length1

Characters and Unicode

Total characters4850
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row3+
4th row2
5th row3+

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1075
29.2%
2 884
24.0%
1 365
 
9.9%
0 182
 
4.9%

Length

2024-01-20T22:35:15.875425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T22:35:15.939891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3 2247
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2247
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
75.8%
Math Symbol 1172
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2247
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2247
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2247
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)1.2%
Missing143
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean7.0347949
Minimum0
Maximum51
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-20T22:35:16.002248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.9785676
Coefficient of variation (CV)0.84985671
Kurtosis4.6158719
Mean7.0347949
Median Absolute Deviation (MAD)3
Skewness1.7150291
Sum24868
Variance35.743271
MonotonicityNot monotonic
2024-01-20T22:35:16.073892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 351
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 814
22.1%
ValueCountFrequency (%)
0 6
 
0.2%
1 351
9.5%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size233.7 KiB
North-East
624 
East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8393468
Min length4

Characters and Unicode

Total characters18008
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth
2nd rowNorth
3rd rowNorth-East
4th rowEast
5th rowNorth-West

Common Values

ValueCountFrequency (%)
North-East 624
17.0%
East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2024-01-20T22:35:16.142563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T22:35:16.212821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
north-east 624
23.7%
east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3776
21.0%
s 2015
11.2%
o 1761
9.8%
h 1761
9.8%
E 1420
 
7.9%
a 1420
 
7.9%
N 1204
 
6.7%
r 1204
 
6.7%
- 1143
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13089
72.7%
Uppercase Letter 3776
 
21.0%
Dash Punctuation 1143
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3776
28.8%
s 2015
15.4%
o 1761
13.5%
h 1761
13.5%
a 1420
 
10.8%
r 1204
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1420
37.6%
N 1204
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16865
93.7%
Common 1143
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3776
22.4%
s 2015
11.9%
o 1761
10.4%
h 1761
10.4%
E 1420
 
8.4%
a 1420
 
8.4%
N 1204
 
7.1%
r 1204
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1143
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3776
21.0%
s 2015
11.2%
o 1761
9.8%
h 1761
9.8%
E 1420
 
7.9%
a 1420
 
7.9%
N 1204
 
6.7%
r 1204
 
6.7%
- 1143
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.6 KiB
Relatively New
1646 
New Property
593 
Moderately Old
563 
Undefined
306 
Old Property
303 

Length

Max length18
Median length14
Mean length13.387167
Min length9

Characters and Unicode

Total characters49238
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowRelatively New
3rd rowOld Property
4th rowRelatively New
5th rowUndefined

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 593
 
16.1%
Moderately Old 563
 
15.3%
Undefined 306
 
8.3%
Old Property 303
 
8.2%
Under Construction 267
 
7.3%

Length

2024-01-20T22:35:16.279243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T22:35:16.344340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
new 2239
31.8%
relatively 1646
23.3%
property 896
12.7%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 306
 
4.3%
under 267
 
3.8%
construction 267
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8432
17.1%
l 4721
 
9.6%
t 3639
 
7.4%
3372
 
6.8%
y 3105
 
6.3%
r 2889
 
5.9%
d 2308
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2219
 
4.5%
Other values (15) 14075
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38816
78.8%
Uppercase Letter 7050
 
14.3%
Space Separator 3372
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8432
21.7%
l 4721
12.2%
t 3639
9.4%
y 3105
 
8.0%
r 2889
 
7.4%
d 2308
 
5.9%
w 2239
 
5.8%
i 2219
 
5.7%
a 2209
 
5.7%
o 1993
 
5.1%
Other values (7) 5062
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2239
31.8%
R 1646
23.3%
P 896
12.7%
O 866
 
12.3%
U 573
 
8.1%
M 563
 
8.0%
C 267
 
3.8%
Space Separator
ValueCountFrequency (%)
3372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45866
93.2%
Common 3372
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8432
18.4%
l 4721
 
10.3%
t 3639
 
7.9%
y 3105
 
6.8%
r 2889
 
6.3%
d 2308
 
5.0%
N 2239
 
4.9%
w 2239
 
4.9%
i 2219
 
4.8%
a 2209
 
4.8%
Other values (14) 11866
25.9%
Common
ValueCountFrequency (%)
3372
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8432
17.1%
l 4721
 
9.6%
t 3639
 
7.4%
3372
 
6.8%
y 3105
 
6.3%
r 2889
 
5.9%
d 2308
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2219
 
4.5%
Other values (15) 14075
28.6%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.0659
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-20T22:35:16.418506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.75
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.25

Descriptive statistics

Standard deviation764.00459
Coefficient of variation (CV)0.39687193
Kurtosis10.355435
Mean1925.0659
Median Absolute Deviation (MAD)372
Skewness1.8373191
Sum3611423.5
Variance583703.02
MonotonicityNot monotonic
2024-01-20T22:35:16.493828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
2408 19
 
0.5%
1900 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (583) 1635
44.5%
(Missing) 1802
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct644
Distinct (%)38.1%
Missing1987
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean2379.0657
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-20T22:35:16.568261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.5
Q11100
median1650
Q32400
95-th percentile4690
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17937.584
Coefficient of variation (CV)7.5397598
Kurtosis1668.8561
Mean2379.0657
Median Absolute Deviation (MAD)650
Skewness40.718593
Sum4023000
Variance3.2175691 × 108
MonotonicityNot monotonic
2024-01-20T22:35:16.642241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
2700 33
 
0.9%
1350 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
2000 24
 
0.7%
1300 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1388
37.7%
(Missing) 1987
54.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct733
Distinct (%)39.2%
Missing1806
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-20T22:35:16.717723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2024-01-20T22:35:16.790503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1450 22
 
0.6%
1000 22
 
0.6%
Other values (723) 1578
42.9%
(Missing) 1806
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2973 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Length

2024-01-20T22:35:16.858440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T22:35:16.917585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

servant room
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2350 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Length

2024-01-20T22:35:16.969551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T22:35:17.028619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

store room
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3340 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Length

2024-01-20T22:35:17.080650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T22:35:17.140686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3022 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Length

2024-01-20T22:35:17.192424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T22:35:17.251132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3273 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Length

2024-01-20T22:35:17.302693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T22:35:17.361616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2416 
1
1056 
2
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2416
65.7%
1 1056
28.7%
2 206
 
5.6%

Length

2024-01-20T22:35:17.413395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T22:35:17.474130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2416
65.7%
1 1056
28.7%
2 206
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 2416
65.7%
1 1056
28.7%
2 206
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2416
65.7%
1 1056
28.7%
2 206
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2416
65.7%
1 1056
28.7%
2 206
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2416
65.7%
1 1056
28.7%
2 206
 
5.6%

luxury_score
Real number (ℝ)

ZEROS 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.511419
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-20T22:35:17.534456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.051945
Coefficient of variation (CV)0.74186676
Kurtosis-0.87958765
Mean71.511419
Median Absolute Deviation (MAD)38
Skewness0.45919115
Sum263019
Variance2814.5088
MonotonicityNot monotonic
2024-01-20T22:35:17.746666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2314
62.9%
ValueCountFrequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2024-01-20T22:35:12.100123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:05.821591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.550551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.223488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.859448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.617906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.306121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.953848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.598314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.437143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:12.167345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:05.897114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.616507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.285474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.930521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.686033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.368002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.015592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.664813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.499914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:12.234486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:05.964658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.683005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.349185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.000800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.754293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.432835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.080010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.732973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.566043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:12.296877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.027967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.745304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.406973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.063556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.818608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.492073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.141554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.803306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.636871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:12.367759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.097882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.816909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.473486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.134867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.891081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.560268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.210317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.940763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.704341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:12.443378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.166418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.889630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.542198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.208204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.964023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.632429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.276914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.108468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.771593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:12.509567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.291228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.953376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.602120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.274183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.030315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.693210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.338651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.173125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.833541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:12.575212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.351390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.019271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.663288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.409385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.093692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.754460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.400787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.233346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.903187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:12.647034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.418790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.089030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.728097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.480083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.164633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.826961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.463849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.305291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.964665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:12.714601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:06.483829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.156052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:07.795073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:08.547589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.236820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:09.889563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:10.533051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:11.367060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-01-20T22:35:12.031243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2024-01-20T22:35:17.898546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.000-0.1170.274-0.145-0.159-0.109-0.0660.0920.0850.215-0.0690.1080.187-0.135-0.1070.3790.2870.1430.140-0.082
area-0.1171.0000.0110.6870.6240.8350.8010.0220.1080.0430.2590.0420.0370.7440.2060.0280.0150.0390.0180.948
balcony0.2740.0111.0000.5150.4340.3560.4680.0160.1600.1790.3280.0820.1970.4650.1870.2140.4410.1460.1820.510
bathroom-0.1450.6870.5151.0000.8620.4650.5990.044-0.0310.1980.1790.0700.2860.7200.4110.4720.5200.2440.1760.819
bedRoom-0.1590.6240.4340.8621.0000.3800.5690.032-0.1360.1680.0570.0790.2910.6810.4170.5950.3170.2230.1540.799
built_up_area-0.1090.8350.3560.4650.3801.0000.9691.0000.0890.0880.2890.0120.0000.6050.1320.0000.0000.0100.0000.926
carpet_area-0.0660.8010.4680.5990.5690.9691.0000.0000.1670.0000.2390.0160.0000.6130.1360.0000.0000.0000.0030.894
facing0.0920.0220.0160.0440.0321.0000.0001.0000.0110.0490.0690.0000.029-0.013-0.0280.0940.0360.0360.0000.019
floorNum0.0850.1080.160-0.031-0.1360.0890.1670.0111.0000.0160.2460.0350.107-0.022-0.1470.5130.0720.1210.0840.147
furnishing_type0.2150.0430.1790.1980.1680.0880.0000.0490.0161.0000.3130.0590.2160.3070.2440.0800.2710.1560.1420.163
luxury_score-0.0690.2590.3280.1790.0570.2890.2390.0690.2460.3131.0000.1760.1890.2150.0540.3290.3470.2280.1830.222
others0.1080.0420.0820.0700.0790.0120.0160.0000.0350.0590.1761.0000.033-0.001-0.0190.0260.0000.1060.031-0.016
pooja room0.1870.0370.1970.2860.2910.0000.0000.0290.1070.2160.1890.0331.0000.2680.1980.2520.2520.3050.3130.110
price-0.1350.7440.4650.7200.6810.6050.613-0.013-0.0220.3070.215-0.0010.2681.0000.7440.5430.3690.3030.2440.772
price_per_sqft-0.1070.2060.1870.4110.4170.1320.136-0.028-0.1470.2440.054-0.0190.1980.7441.0000.2010.0440.0000.0300.287
property_type0.3790.0280.2140.4720.5950.0000.0000.0940.5130.0800.3290.0260.2520.5430.2011.0000.0650.2410.128NaN
servant room0.2870.0150.4410.5200.3170.0000.0000.0360.0720.2710.3470.0000.2520.3690.0440.0651.0000.1610.1850.657
store room0.1430.0390.1460.2440.2230.0100.0000.0360.1210.1560.2280.1060.3050.3030.0000.2410.1611.0000.2260.033
study room0.1400.0180.1820.1760.1540.0000.0030.0000.0840.1420.1830.0310.3130.2440.0300.1280.1850.2261.000-0.015
super_built_up_area-0.0820.9480.5100.8190.7990.9260.8940.0190.1470.1630.222-0.0160.1100.7720.287NaN0.6570.033-0.0151.000

Missing values

2024-01-20T22:35:12.842645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-20T22:35:13.064090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-20T22:35:13.322180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatvatika gurgaonsector 831.406628.02112.25Super Built up area 1735(161.19 sq.m.)3327.0NaNRelatively New1735.0NaNNaN00000174
1flatrof aalayassector 1020.458404.0535.46Super Built up area 597(55.46 sq.m.)Carpet area: 535.4 sq.ft. (49.74 sq.m.)22114.0NorthRelatively New597.0NaN535.400000257
2flatbestech park view residencysector 21.407865.01780.04Super Built up area 1780(165.37 sq.m.)333+6.0NorthOld Property1780.0NaNNaN000002107
3flatla vida by tata housingsector 1131.6510449.01579.10Super Built up area 1579(146.69 sq.m.)Built Up area: 1420 sq.ft. (131.92 sq.m.)Carpet area: 1220 sq.ft. (113.34 sq.m.)3223.0North-EastRelatively New1579.01420.01220.000000049
4flatm3m heightssector 652.1515003.01433.05Built Up area: 1433 (133.13 sq.m.)223+28.0EastUndefinedNaN1433.0NaN00000048
5flatsupertech aravillesector 790.805266.01519.18Carpet area: 1519 (141.12 sq.m.)22212.0NaNNew PropertyNaNNaN1519.010000049
6flatdlf new town heightssector 901.155958.01930.18Super Built up area 1930(179.3 sq.m.)333+9.0North-WestRelatively New1930.0NaNNaN01010088
7houseindependentsector 494.5015625.02880.00Plot area 320(267.56 sq.m.)8833.0North-EastModerately OldNaN2880.0NaN111102151
8flatemaar mgf the palm drivesector 663.0015384.01950.08Super Built up area 1950(181.16 sq.m.)Built Up area: 1720 sq.ft. (159.79 sq.m.)Carpet area: 1620 sq.ft. (150.5 sq.m.)33212.0EastRelatively New1950.01720.01620.001000096
9houseindependentsector 235.5013095.04200.00Plot area 4200(390.19 sq.m.)993+3.0SouthModerately OldNaN4200.0NaN0100110
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3792flatdlf new town heightssector 911.656345.02600.47Super Built up area 2364(219.62 sq.m.)443+11.0WestModerately Old2364.0NaNNaN01000072
3794flattulip violetsector 691.559822.01578.09Super Built up area 1578(146.6 sq.m.)332NaNWestRelatively New1578.0NaNNaN000101129
3795flatvatika the seven lampssector 821.396412.02167.81Super Built up area 2160(200.67 sq.m.)333+11.0EastRelatively New2160.0NaNNaN110001117
3796flatunitech sunbreezesector 691.005476.01826.15Built Up area: 1826 (169.64 sq.m.)430NaNNaNUndefinedNaN1826.0NaN0000000
3797flatrof anandasector 950.266842.0380.01Carpet area: 380 (35.3 sq.m.)1118.0NorthNew PropertyNaNNaN380.000000049
3798flatdlf the ultimasector 812.3411185.02092.09Super Built up area 2092(194.35 sq.m.)Built Up area: 1760 sq.ft. (163.51 sq.m.)Carpet area: 1670 sq.ft. (155.15 sq.m.)343+14.0South-EastRelatively New2092.01760.01670.0010002174
3799flatbestech park view grand spasector 812.358834.02660.18Super Built up area 2660(247.12 sq.m.)Carpet area: 2356 sq.ft. (218.88 sq.m.)343+6.0North-EastRelatively New2660.0NaN2356.0010001101
3800houseeldeco mansionzsector 484.7520299.02340.00Plot area 260(217.39 sq.m.)7732.0North-EastModerately OldNaN2340.0NaN10000133
3801flattulip violetsector 691.909452.02010.16Super Built up area 2010(186.74 sq.m.)Carpet area: 1500 sq.ft. (139.35 sq.m.)4426.0WestRelatively New2010.0NaN1500.0001000174
3802flatireo skyonsector 603.7513392.02800.18Super Built up area 2800(260.13 sq.m.)Built Up area: 2650 sq.ft. (246.19 sq.m.)Carpet area: 2350 sq.ft. (218.32 sq.m.)44312.0NorthRelatively New2800.02650.02350.001000149